Real-Time Risk Assessment In Business Operations Using Bayesian Networks

Abstract

In today's business world, there are various challenges that are dynamic and unpredictable, which require real-time risk management in order to reduce business losses. The aim of this paper is to develop a Bayesian Network-based real-time risk assessment approach to improve business decision-making by continuously updating the risk assessment based on continuous data streams. Established risk management practices, which are based on static or lagged models, cannot address the dynamism of today's business risks. The suggested approach is to work with Bayesian Networks for probabilistic modeling, which allow for quantification of uncertainty, and the modeling of complex interdependencies between the risk factors. The model is dynamically adjusted, taking into account the online learning techniques. The framework is evaluated using synthetic and operational data of the business and achieves substantial performance gains over a baseline model. More precisely, the accuracy, precision, recall, and F1 score obtained by the RT-BNRA model are 92.5%, 91.3%, 93.2% and 92.3%, respectively. These results outperform other techniques such as logistic regression (accuracy of 84.6%) and Naïve Bayes (accuracy of 81.2%). The risk score calculation is done on the basis of the latest network probabilities in real-time, which enables risk alert generation and decision support. The purpose of this paper was to prove that Bayesian networks can be utilized in real-time business risk management, providing valuable insights into real-time decision-making scenarios.